ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation

Abstract

In medical and industrial domains, providing guidance for assembly processes is critical to ensure efficiency and safety. Errors in assembly can lead to significant consequences such as extended surgery times, and prolonged manufacturing or maintenance times in industry. Assembly scenarios can benefit from in-situ Augmented Reality (AR) visualization to provide guidance, reduce assembly times and minimize errors. To enable in-situ visualization 6D pose estimation can be leveraged. Existing 6D pose estimation techniques primarily focus on individual objects and static captures. However, assembly scenarios have various dynamics including occlusion during assembly and dynamics in the assembly objects appearance. Existing work, combining object detection/6D pose estimation and assembly state detection focuses either on pure deep learning-based approaches, or limit the assembly state detection to building blocks. To address the challenges of 6D pose estimation in combination with assembly state detection, our approach ASDF builds upon the strengths of YOLOv8, a real-time capable object detection framework. We extend this framework, refine the object pose and fuse pose knowledge with network-detected pose information. Utilizing our late fusion in our Pose2State module results in refined 6D pose estimation and assembly state detection. By combining both pose and state information, our Pose2State module predicts the final assembly state with precision. Our evaluation on our ASDF dataset shows that our Pose2State module leads to an improved assembly state detection and that the improvement of the assembly state further leads to a more robust 6D pose estimation. Moreover, on the GBOT dataset, we outperform the pure deep learning-based network, and even outperform the hybrid and pure tracking-based approaches.